定位移动机器人的一种常见方法是测量已知位置点的距离，称为锚点。从距离测量值中定位设备通常是由于测量模型的非线性而作为非凸优化问题。当使用局部迭代求解器（如高斯 - 牛顿）时，非凸优化问题可能会产生次优的解决方案。在本文中，我们为连续范围的本地化设计了最佳证书。我们的公式可以整合运动，从而确保溶液的平滑度，并且对于仅从几个距离测量值进行定位至关重要。拟议的证书几乎没有额外的成本，因为它的复杂性与稀疏本地求解器本身的复杂性相同：位置数量的线性。我们在仿真和现实世界数据集中显示，有效的本地求解器通常会找到全球最佳解决方案（通过我们的证书确认），而当没有证书确认时，简单的随机重新初始化最终会导致可认证的最佳选择。

近年来，大型预训练的变压器网络已显示出许多自然语言理解任务的巨大改进。但是，由于延迟和成本限制，这些模型的巨大规模给他们的微调和在线部署带来了重大挑战。支持N：M半结构化的稀疏性和低精油整数计算的新硬件是提高DNN模型效率的有前途解决方案。但是，很少有研究系统地研究预先训练的变压器网络在多大程度上受益于这些技术的组合，以及如何最好地压缩变压器的每个组件。我们提出了一个灵活的压缩框架NXMiformer，该框架使用ADMM和基于Ste的QAT执行同时进行稀疏和量化。此外，我们介绍且廉价的启发式驱动搜索算法，该算法标识了满足压缩比约束的有希望的异质压缩配置。当通过NLU基准测试的胶水套件进行评估时，我们的方法可以达到BERT模型编码器的93％压缩，同时保留了98.2％的原始模型准确性并充分利用硬件功能。异质配置通过搜索启发式发现了基线准确性的99.5％，同时仍将模型压缩为87.5％。

现代状态估计通常被表达为优化问题，并使用有效的本地搜索方法解决。这些方法最能保证与本地最小值的融合，但是在某些情况下，全球最优性也可以得到认证。尽管此类全球最佳证书已经为3D姿势格言优化确定了，但是对于基于3D地标的SLAM问题，尚未确定细节，其中估计的状态包括机器人姿势和地图地标。在本文中，我们通过使用图理论方法来解决这一差距，将基于里程碑的SLAM的子问题投入到一种形式，该形式产生了足够的全球最优状态。存在计算这些子问题的最佳证书的有效方法，但首先需要构建大型数据矩阵。我们表明，该矩阵可以以复杂性构建，该复杂性在地标数量中保持线性，并且不超过一个局部求解器的最新计算复杂性。最后，我们证明了证书对基于模拟和现实世界标记的大满贯问题的功效。

本说明使用总平方（TLS）线路拟合问题作为探索一些现代优化工具的画布。该贡献本质上是教程。TLS问题与机器人技术和计算机视觉中的重要问题具有很大的数学相似性，但更易于可视化和理解。我们演示了如何将此问题转变为四二次二次程序（QCQP），以便可以将其作为本本特征问题或半明确程序（SDP）施放。然后，我们转向更具挑战性的情况，在这种情况下，Geman-McClure成本函数和M估计用于拒绝离群数据标记。使用Black-rangarajan二元性，我们表明它也可以施加为QCQP并将其求解为SDP。但是，有了大量数据，SDP可能会很慢，因此我们展示了如何为更快的方法（例如迭代重新加权最小二乘（IRLS））构建最佳证书。

Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explore this concept, we have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. Our analysis reveals that traditional loss functions can struggle to effectively incorporate the DAG structure, leading us to propose a causality-enhanced method called Exponential Maximum Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models. To evaluate the effectiveness of this approach, we have built a comprehensive benchmark using the CausalDialogue dataset leveraging large-scale pre-trained language models, and have assessed the results through both human and automatic evaluation metrics for coherence, diversity, and agility. Our findings show that current techniques are still unable to effectively address conversational DAGs, and that the ExMATE method can improve the diversity and agility of conventional loss functions while maintaining coherence.

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low $(0<z<0.25)$, medium $(0.25<z<0.5)$, and high $(0.5<z<1.0)$. By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin. Specifically, our models achieve disk precision of $96\%/82\%/79\%$ and bulge precision of $90\%/90\%/80\%$ (for the 3 redshift bins), at thresholds corresponding to indeterminate fractions of $30\%/43\%/42\%$. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging survey.

Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean \textit{k}-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines.